
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :		Optional[Any]       			=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :		Optional[Any]       			=							{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}






class        __lowercase		(       _A							):
					__magic_name__    :				List[Any]  						=						'''mgp-str'''


					def __init__(						self							,							a__=[3_2, 1_2_8]							,							a__=4							,							a__=3							,							a__=2_7							,							a__=3_8							,							a__=5_0_2_5_7							,							a__=3_0_5_2_2							,							a__=7_6_8							,							a__=1_2							,							a__=1_2							,							a__=4.0							,							a__=True							,							a__=False							,							a__=1E-5							,							a__=0.0							,							a__=0.0							,							a__=0.0							,							a__=False							,							a__=0.02							,							**a__							,							)		->				str:





									'''simple docstring'''
									super().__init__(**UpperCamelCase__    )

									A_													=	image_size
									A_													=	patch_size
									A_													=	num_channels
									A_													=	max_token_length
									A_													=	num_character_labels
									A_													=	num_bpe_labels
									A_													=	num_wordpiece_labels
									A_													=	hidden_size
									A_													=	num_hidden_layers
									A_													=	num_attention_heads
									A_													=	mlp_ratio
									A_													=	distilled
									A_													=	layer_norm_eps
									A_													=	drop_rate
									A_													=	qkv_bias
									A_													=	attn_drop_rate
									A_													=	drop_path_rate
									A_													=	output_aa_attentions
									A_													=	initializer_range

import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowerCAmelCase     :	Dict       			=  'src/transformers'
__lowerCAmelCase     :	List[Any]       			=  'docs/source/en'
__lowerCAmelCase     :	str       			=  '.'

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  with open(A_,	"""r""",	encoding="""utf-8""",	newline="""\n"""   ) as f:
    __magic_name__								=			f.readlines()
  # Find the start prompt.
  __magic_name__								=			0
  while not lines[start_index].startswith(A_   ):
    start_index += 1
  start_index += 1

  __magic_name__								=			start_index
  while not lines[end_index].startswith(A_   ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]   ) <= 1:
    start_index += 1
  while len(lines[end_index]   ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__lowerCAmelCase     :	str       			=  'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase     :	List[str]       			=  re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__lowerCAmelCase     :	Any       			=  re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCAmelCase     :	Optional[int]       			=  re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase     :	Union[str, Any]       			=  direct_transformers_import(TRANSFORMERS_PATH)

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""",	A_   )
  return [m.group(0   ) for m in matches]

def a__       (			A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			2 if text == """✅""" or text == """❌""" else len(A_   )
  __magic_name__								=			(width - text_length) // 2
  __magic_name__								=			width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  __magic_name__								=			{
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  __magic_name__								=			{name: config.replace("""Config""",	""""""   ) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )

  # Let's lookup through all transformers object (once).
  for attr_name in dir(A_   ):
    __magic_name__								=			None
    if attr_name.endswith("""Tokenizer"""   ):
      __magic_name__								=			slow_tokenizers
      __magic_name__								=			attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""   ):
      __magic_name__								=			fast_tokenizers
      __magic_name__								=			attr_name[:-13]
    elif _re_tf_models.match(A_   ) is not None:
      __magic_name__								=			tf_models
      __magic_name__								=			_re_tf_models.match(A_   ).groups()[0]
    elif _re_flax_models.match(A_   ) is not None:
      __magic_name__								=			flax_models
      __magic_name__								=			_re_flax_models.match(A_   ).groups()[0]
    elif _re_pt_models.match(A_   ) is not None:
      __magic_name__								=			pt_models
      __magic_name__								=			_re_pt_models.match(A_   ).groups()[0]

    if lookup_dict is not None:
      while len(A_   ) > 0:
        if attr_name in model_name_to_prefix.values():
          __magic_name__								=			True
          break
        # Try again after removing the last word in the name
        __magic_name__								=			"""""".join(camel_case_split(A_   )[:-1]   )

    # Let's build that table!
  __magic_name__								=			list(model_name_to_config.keys()   )
  model_names.sort(key=str.lower   )
  __magic_name__								=			["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  __magic_name__								=			[len(A_   ) + 2 for c in columns]
  __magic_name__								=			max([len(A_   ) for name in model_names]   ) + 2

  # Build the table per se
  __magic_name__								=			"""|""" + """|""".join([_center_text(A_,	A_   ) for c, w in zip(A_,	A_   )]   ) + """|\n"""
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]   ) + "|\n"

  __magic_name__								=			{True: """✅""", False: """❌"""}
  for name in model_names:
    __magic_name__								=			model_name_to_prefix[name]
    __magic_name__								=			[
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(A_,	A_   ) for l, w in zip(A_,	A_   )]   ) + "|\n"
  return table

def a__       (			A_=False   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			_find_text_in_file(
      filename=os.path.join(A_,	"""index.md"""   ),	start_prompt="""<!--This table is updated automatically from the auto modules""",	end_prompt="""<!-- End table-->""",	)
  __magic_name__								=			get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(A_,	"""index.md"""   ),	"""w""",	encoding="""utf-8""",	newline="""\n"""   ) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]   )
    else:
      raise ValueError(
          """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Tuple       			=  argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    __lowerCAmelCase     :	Optional[Any]       			=  parser.parse_args()

    check_model_table(args.fix_and_overwrite)

